Biology subjects

Vanguri, R.

Publications and source records attributed to Vanguri, R..

6 recordsLinked to original sources

bioRxiv2025

A Novel Therapeutic Approach: Notch Inhibition Enhances Radiotherapy and Checkpoint Blockade Therapy via Reprogramming of the Tumor Microenvironment

BackgroundHigh-dose radiotherapy (RT) in cancer is immunogenic but also induces an immunosuppressive tumor microenvironment (TME) that limits the efficacy of immune checkpoint inhibitors (ICIs). Overcoming this immunosuppressive barrier is therefore critical to unlocking the full potential of RT-ICI combinations. As Notch signaling regulates tumor microenvironment, we hypothesized that the {gamma}-secretase inhibitor (GSI), AL101, would suppress radiation-induced immunosuppression and enhance antitumor efficacy when combined with RT and anti-PD-1 (aPD-1) therapy. MethodsSyngeneic neuroblastoma (9464D) and triple-negative breast cancer (EO771) tumors were established in C57BL/6, macrophage-depleted C57BL/6 mice, or athymic mice. Mice received 12 Gy RT (day 3), AL101 (6.5 mg/kg daily, day 0-9), and aPD-1 (days 0, 3, 6). Tumors were analyzed by spectral flow cytometry and single-cell RNA sequencing (scRNA-seq), and lung metastases were evaluated histologically. ResultsThe triple combination of RT, aPD-1, and GSI produced durable tumor growth inhibition and significantly prolonged survival in both models, with median survival more than doubled compared to all other treatment groups. Triple therapy also markedly reduced lung metastases in EO771 mice. These effects were abrogated in athymic nude mice and macrophage-depleted immunocompetent mice, consistent with an immune-dependent mechanism. Based on scRNA-seq and spectral flow cytometry analysis, RT alone increased exhausted T cells and immunosuppressive macrophages, while triple therapy reversed these effects, including expansion of activated CD8 T cells, reduction of Tregs and exhausted T cells, restoration of cross-presenting CD103 dendritic cells, and reprogramming of myeloid cells toward a proinflammatory state. ConclusionsGSI remodels the RT-induced immunosuppressive TME and potentiates the efficacy of RT + ICI. GSI combined with RT+aPD-1 reprograms the TME toward an immunostimulatory state and supports GSI as a promising immuno-radiotherapeutic strategy with strong translational potential.

cancer biology↗

bioRxiv2024

Automated classification of cellular expression in multiplexed imaging data with Nimbus

Multiplexed imaging offers a powerful approach to characterize the spatial topography of tissues in both health and disease. To analyze such data, the specific combination of markers that are present in each cell must be enumerated to enable accurate phenotyping, a process that often relies on unsupervised clustering. We constructed the Pan-Multiplex (Pan-M) dataset containing 197 million distinct annotations of marker expression across 15 different cell types. We used Pan-M to create Nimbus, a deep learning model to predict marker positivity from multiplexed image data. Nimbus is a pre-trained model that uses the underlying images to classify marker expression across distinct cell types, from different tissues, acquired using different microscope platforms, without requiring any retraining. We demonstrate that Nimbus predictions capture the underlying staining patterns of the full diversity of markers present in Pan-M. We then show how Nimbus predictions can be integrated with downstream clustering algorithms to robustly identify cell subtypes in image data. We have open-sourced Nimbus and Pan-M to enable community use at https://github.com/angelolab/Nimbus-Inference.

bioinformatics↗

bioRxiv2024

A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma

Pediatric high-grade glioma (pHGG) is an incurable central nervous system malignancy that is a leading cause of pediatric cancer death. While pHGG shares many similarities to adult glioma, it is increasingly recognized as a molecularly distinct, yet highly heterogeneous disease. In this study, we longitudinally profiled a molecularly diverse cohort of 16 pHGG patients before and after standard therapy through single-nucleus RNA and ATAC sequencing, whole-genome sequencing, and CODEX spatial proteomics to capture the evolution of the tumor microenvironment during progression following treatment. We found that the canonical neoplastic cell phenotypes of adult glioblastoma are insufficient to capture the range of tumor cell states in a pediatric cohort and observed differential tumor-myeloid interactions between malignant cell states. We identified key transcriptional regulators of pHGG cell states and did not observe the marked proneural to mesenchymal shift characteristic of adult glioblastoma. We showed that essential neuromodulators and the interferon response are upregulated post-therapy along with an increase in non-neoplastic oligodendrocytes. Through in vitro pharmacological perturbation, we demonstrated novel malignant cell-intrinsic targets. This multiomic atlas of longitudinal pHGG captures the key features of therapy response that support distinction from its adult counterpart and suggests therapeutic strategies which are targeted to pediatric gliomas.

cancer biology↗

bioRxiv2021

Immune and malignant cell phenotypes of ovarian cancer are determined by distinct mutational processes

High-grade serous ovarian cancer (HGSOC) is an archetypal cancer of genomic instability patterned by distinct mutational processes, intratumoral heterogeneity and intraperitoneal spread. We investigated determinants of immune recognition and evasion in HGSOC to elucidate co- evolutionary processes underlying malignant progression and tumor immunity. Mutational processes and anatomic sites of tumor foci were key determinants of tumor microenvironment cellular phenotypes, inferred from whole genome sequencing, single-cell RNA sequencing, digital histopathology and multiplexed immunofluorescence of 160 tumor sites from 42 treatment-naive HGSOC patients. Homologous recombination-deficient (HRD)-Dup (BRCA1 mutant-like) and HRD- Del (BRCA2 mutant-like) tumors harbored increased neoantigen burden, inflammatory signaling and ongoing immunoediting, reflected in loss of HLA diversity and tumor infiltration with highly- differentiated dysfunctional CD8+ T cells. Foldback inversion (FBI, non-HRD) tumors exhibited elevated TGF{beta} signaling and immune exclusion, with predominantly naive/stem-like and memory T cells. Our findings implicate distinct immune resistance mechanisms across HGSOC subtypes which can inform future immunotherapeutic strategies. HIGHLIGHTSO_LIMulti-region, multi-modal profiling of malignant and immune cell phenotypes in ovarian cancer C_LIO_LIAnatomic site specificity is a determinant of cancer cell and intratumoral immune phenotypes C_LIO_LITumor mutational processes impact mechanisms of immune control and immune evasion C_LIO_LISpatial topology of HR-deficient tumors is defined by immune interactions absent from immune inert HR-proficient subtypes C_LI

cancer biology↗

bioRxiv2021

DeepLIIF: Deep Learning-Inferred Multiplex ImmunoFluorescence for IHC Quantification

Reporting biomarkers assessed by routine immunohistochemical (IHC) staining of tissue is broadly used in diagnostic pathology laboratories for patient care. To date, clinical reporting is predominantly qualitative or semi-quantitative. By creating a multitask deep learning framework referred to as DeepLIIF, we present a single-step solution to stain deconvolution/separation, cell segmentation, and quantitative single-cell IHC scoring. Leveraging a unique de novo dataset of co-registered IHC and multiplex immunofluorescence (mpIF) staining of the same slides, we segment and translate low-cost and prevalent IHC slides to more expensive-yet-informative mpIF images, while simultaneously providing the essential ground truth for the superimposed brightfield IHC channels. Moreover, a new nuclear-envelop stain, LAP2beta, with high (>95%) cell coverage is introduced to improve cell delineation/segmentation and protein expression quantification on IHC slides. By simultaneously translating input IHC images to clean/separated mpIF channels and performing cell segmentation/classification, we show that our model trained on clean IHC Ki67 data can generalize to more noisy and artifact-ridden images as well as other nuclear and non-nuclear markers such as CD3, CD8, BCL2, BCL6, MYC, MUM1, CD10, and TP53. We thoroughly evaluate our method on publicly available benchmark datasets as well as against pathologists semi-quantitative scoring. The code, the pre-trained models, along with easy-to-run containerized docker files as well as Google CoLab project are available at https://github.com/nadeemlab/deepliif.

bioinformatics↗

bioRxiv2019

Predicting the genetic ancestry of 2.6 million New York City patients using clinical data

Ancestry is an essential covariate in clinical genomics research. When genetic data are available, dimensionality reduction techniques, such as principal components analysis, are used to determine ancestry in complex populations. Unfortunately, these data are not always available in the clinical and research settings. For example, electronic health records (EHRs), which are a rich source of temporal human disease data that could be used to enhance genetic studies, do not directly capture ancestry. Here, we present a novel algorithm for predicting genetic ancestry using only variables that are routinely captured in EHRs, such as self-reported race and ethnicity, and condition billing codes. Using patients that have both genetic and clinical information at Columbia University/ New York-Presbyterian Irving Medical Center, we developed a pipeline that uses only clinical data to predict the genetic ancestry of all patients of which more than 80% identify as other or unknown. Our ancestry estimates can be used in observational studies of disease inheritance, to guide genetic cohort studies, or to explore health disparities in clinical care and outcomes.

bioinformatics↗